The Case for Self-Hosting Large Language Models in Enterprise AI

There is a common misconception, especially in the enterprise context, that implementing powerful AI use-cases without relying on external AI providers, such as OpenAI, Anthropic, or Google, is not feasible or even possible. Many believe that running powerful Large Language Models (LLMs) requires massive amounts of compute or that the models you can run yourself are not powerful enough for most use-cases.

In this white paper we highlight how recent advancements in open-source LLMs and hardware efficiency have made self-hosting these models for enterprise AI use-cases not just possible, but highly feasible for companies of all sizes.

In particular, we will answer the following questions:

1. What is the current state of open-source LLMs and how does their performance compare to proprietary models?
2. What are the benefits of self-hosting LLMs rather than relying on cloud-based services?
3. What are the practical considerations for implementation, including hardware requirements and integration strategies?
4. How can self-hosted LLMs be integrated into existing enterprise workflows and systems?

Recent Advancements in Self-Hosted LLMs

Until very recently, running powerful LLMs on-premise was considered impossible due to extensive computational requirements, the need for specialized knowledge, and the lack of capable open-source models available for commercial use. However, the landscape has dramatically shifted. With the release of Meta Llama 3.1, there is no more question that open-source models have become powerful enough to rival their proprietary counterparts, such as GPT-4o, Claude 3.5, and Gemini 1.5 Pro.¹

These high-performance open-source models, coupled with more powerful and efficient hardware, have significantly lowered entry barriers for self-hosting LLMs. This shift allows organizations to leverage state-of-the-art AI capabilities while maintaining complete control over their data and infrastructure.

Open-Source LLMs Have Become a Viable Alternative

This chart illustrates the narrowing gap between open-source and proprietary models in reasoning and knowledge capabilities. The release of Llama 3.1 in mid 2024 marked a significant milestone, achieving performance parity with leading commercial offerings in many benchmark tests, including MMLU.² This development represents a turning point in LLM accessibility and quality, making self-hosted models a viable option for enterprise use.³

Resources like the Hugging Face Hub provide a comprehensive repository of open-source models, while tools such as vLLM and Ollama simplify the process of running these high-performance LLMs on standard enterprise infrastructure.⁴⁵⁶

Hardware Considerations for Self Hosting LLMs

While self-hosting LLMs still requires significant computing power, advancements in model efficiency and hardware utilization have made it feasible for many enterprises to host their own models. Recent improvements in GPU efficiency, such as enhanced attention mechanisms, have significantly reduced the computational requirements for running large language models.⁷ Organizations can now run powerful models on more modest GPU setups, significantly reducing the barrier to entry and setup costs.

Key Benefits of Self-Hosting LLMs

Hosting LLMs on-premise is particularly valuable for industries dealing with private data and sensitive information, including:

• Regulated industries, such as healthcare, finance, and legal
• Organizations with trade secrets or proprietary information
• Research institutions handling confidential datasets and results

Self-hosting LLMs offers significant advantages, especially for companies and organizations dealing with sensitive information or operating in regulated industries. These key benefits include:

• Enhanced Data Privacy and Security: Self-hosting allows for complete control over data, crucial for protecting sensitive information
• Regulatory Compliance: Industries subject to GDPR, HIPAA, and other regulatory oversight find self-hosting provides a clear path to compliance
• Reduced External Dependency: Organizations can mitigate risks associated with vendor lock-in and service disruptions while accessing cutting-edge AI technology.

Recent studies have highlighted significant privacy concerns with proprietary LLMs in professional settings. These include risks of data leakage, lack of transparency in data handling, and potential misuse of sensitive information. Such concerns underscore the importance of self-hosted solutions for enterprises dealing with confidential data.⁸

Open-Source vs. Cloud-Hosted LLMs

To better understand the trade-offs between self-hosted open-source LLMs and cloud-based proprietary LLMs, let's examine a side-by-side comparison of their key features and characteristics. This table highlights the main differences in areas such as data privacy, compliance, performance, costs, and technical requirements, helping organizations make informed decisions about their AI infrastructure strategy.

Omnifact's Role in Self-Hosted LLMs

Up to this point, we have only talked about LLMs as part of the enterprise AI infrastructure. However, in order to effectively automate enterprise workflows and empower employees with capable AI assistants, these models need to be deeply integrated into an organization's existing infrastructure and made accessible to employees of all technical skill levels.

Omnifact offers a comprehensive solution for enterprises to run, integrate, and build upon self-hosted open-source LLMs, addressing many of the challenges organizations face. We provide companies with:

• A secure, model-independent foundation for AI use-cases
• A user-friendly conversational interface and application layer that makes AI accessible to non-technical users
• Deep integrations into internal data sources and core enterprise systems, such as ERP, CRM, and DMS
• Custom AI assistants that can automate workflows and access internal systems and data through natural language
• Ongoing support for maintenance, updates, and optimizations

Successful integration of AI into enterprise workflows requires careful planning and organizational changes, making solutions that offer seamless integration particularly valuable.⁹

Challenges and Considerations

While self-hosting offers numerous advantages, organizations should be aware of potential challenges:

• Technical Expertise: Rolling out and maintaining self-hosted LLMs requires specialized knowledge in AI, infrastructure, and data management. Omnifact offers enterprise customers support in setting up and maintaining their self-hosted models.
• Initial Setup Costs: Though long-term expenses may be lower, the upfront investment in hardware and infrastructure can be substantial, which needs to be taken into account when deciding on your AI strategy.
• Hardware Access: Obtaining access to high-performance GPUs can be challenging, especially given current market demands and supply chain issues. The current high demand for AI-specific GPUs has led to significant shortages, potentially impacting setup timelines and initial costs.¹⁰
• User Acceptance and Trust: Ensuring that non-technical users trust and effectively utilize AI-driven solutions can be a challenge. Providing intuitive interfaces and clear benefits can significantly improve user adoption and confidence.
• Maintaining Implemented Use-Cases: As technology evolves, maintaining and optimizing AI use-cases to stay current with business needs requires ongoing effort. This can be managed effectively with a well-defined support and maintenance strategy.

The Future of Enterprise AI — Secure, Accessible, On-Premise

Self-hosting open-source models has become a powerful alternative for organizations to leverage state-of-the-art AI while maintaining strict control over their data and infrastructure. The performance gap between open-source and proprietary models has essentially closed, making self-hosting an increasingly attractive option for enterprises of any size, especially in regulated industries.

As open-source AI technology continues to rapidly evolve, we anticipate even more sophisticated agentic AI models capable of complex decision-making and seamless integration with internal services. Self-hosted LLMs will play a crucial role in this evolution, especially for organizations prioritizing both cutting-edge capabilities and control over their data.

With solutions like Omnifact, organizations can effectively self-host LLMs, unlocking AI-driven innovation securely and compliantly, without sacrificing model quality or capabilities.

This whitepaper has been created and brought to you by Omnifact. Omnifact is a privacy-first generative AI platform that empowers enterprises to leverage AI for productivity and automation while maintaining complete control over their sensitive data. By offering secure, customizable AI assistants and workflow automation solutions deployable on-premise or in private cloud environments, Omnifact enables organizations in regulated industries and those prioritizing data sovereignty to unlock the power of AI without compromising on security or compliance.

If you have any questions about this whitepaper or if Omnifact can help you with your AI needs, please reach out to us at hello@omnifact.ai.